Unified cardinalized probability hypothesis density filters for extended targets and unresolved targets

Liu, Feng; Han, Chongzhao; Liu, Weifeng; Liu, Jing; Sun, Jian

doi:10.1016/j.sigpro.2012.01.009

Cited by 28 publications

(26 citation statements)

References 30 publications

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“…The Monte Carlo implementation of the formulas (14) and (15) could be described as follows: assuming that a set of samples with weights { ,( )…”

Section: The Framework Of the Multiple Extended Target Particlementioning

confidence: 99%

“…For example, in [11], Mahler presents an extension of the probability hypothesis density (PHD) filter [12] to handle extended targets. Orguner et al propose a cardinalized probability hypothesis density (CPHD) filter for extended 2 Mathematical Problems in Engineering targets [13], and Lian et al propose unified CPHD filters for extended targets and unresolved targets [14]. Based on the Gaussian-mixture PHD filter [15], Granstrom presents the extended target GMPHD (ET-GMPHD) filter for extended target tracking in [16] and in [17] describes much more details and extensive investigations of the methodology.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian Approach to Track Multiple Extended Targets Using Particle Filter for Nonlinear System

Han

2018

Mathematical Problems in Engineering

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To track multiple extended targets for the nonlinear system, this paper employs the idea of the particle filter to track kinematic states and shape formation of extended targets. First, the Bayesian framework is proposed for multiple extended targets to jointly estimate multiple extended target state and association hypothesis. Furthermore, a joint proposal distribution is defined for the multiple extended target state and association hypothesis. Then, the Bayesian framework of multiple extended target tracking is implemented by the particle filtering which could release the high computational burden caused by the increase in the number of extended targets and measurements. Simulation results show that the proposed multiple extended target particle filter has superior performance in shape estimation and improves the performance of the position estimation in the situation that there are spatially closed extended targets.

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“…The Monte Carlo implementation of the formulas (14) and (15) could be described as follows: assuming that a set of samples with weights { ,( )…”

Section: The Framework Of the Multiple Extended Target Particlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to Track Multiple Extended Targets Using Particle Filter for Nonlinear System

Han

2018

Mathematical Problems in Engineering

Self Cite

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“…Typical methods for extended/group target tracking include random matrix framework [205,206], PHD/CPHD filter [207][208][209], Bernoulli filter [210,211]. More researches need to be undertaken in future works, especially that the estimated shape of the target is an important piece of information that has to be incorporated into the entire tracking procedure.…”

Section: Extended/group Target Trackingmentioning

confidence: 99%

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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Abstract-Multitarget tracking (MTT) in surveillance system is extremely challenging, due to uncertain data association, maneuverable target motion, dense clutter disturbance, and real-time processing requirements. A good many methods have been proposed to cope with these challenges. However, no up-to-date survey is available in the literature that can help to select suitable tracking algorithm for practical problem. This paper provides a comprehensive review of the state-of-the-art motion-based MTT techniques, classifies existing methods into two groups, i.e., the detect-beforetrack (DBT) scheme and the track-before-detect (TBD) scheme. The DBT scheme is employed to achieve robust and tractable tracking performance when the signal-noise-ratio (SNR) is strong. The TBD scheme is used in the scenarios of low SNR, and it aims to cumulate target energy by multiple sensor frames. Furthermore, depending on the data association mechanism, the DBT methods can be classified into two categories, data association based approaches and finite set statistics (FISST) based approaches. And the TBD methods can be classified into non-Bayesian approaches and Bayesian approaches depending on the basis theory used for tracking. For each category, this paper provides the detailed descriptions of the representative algorithms, and examines their pros and cons. Finally, new trends for future research directions are offered.

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“…The models used in [32] were later used to derive a PHD filter in [25]. A CPHD filter for extended targets was presented in [33], however the filter derivation is based on the quite strong assumption that "relative to sensor resolution, the extended targets and the unresolved targets are not too close and the clutter density is not too large" [33,Corollary 1]. This assumption cannot be expected to hold in the general case.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, it is shown in the results section that the presented filter handles both high clutter density and spatially close targets. In this sense the presented extended target CPHD filter is more general than the CPHD filter presented in [33]. The rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation

Lundquist

Granström

Orguner

2013

IEEE J. Sel. Top. Signal Process.

139

123

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Abstract-This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has been derived by Mahler, and different implementations have been proposed recently. To achieve better estimation performance this work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers. A gamma Gaussian inverse Wishart mixture implementation, which is capable of estimating the target extents and measurement rates as well as the kinematic state of the target, is proposed, and it is compared to its PHD counterpart in a simulation study. The results clearly show that the CPHD filter has a more robust cardinality estimate leading to smaller OSPA errors, which confirms that the extended target CPHD filter inherits the properties of its point target counterpart.

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Unified cardinalized probability hypothesis density filters for extended targets and unresolved targets

Cited by 28 publications

References 30 publications

A Bayesian Approach to Track Multiple Extended Targets Using Particle Filter for Nonlinear System

A Bayesian Approach to Track Multiple Extended Targets Using Particle Filter for Nonlinear System

A Survey of Motion-Based Multitarget Tracking Methods

An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation

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